diff --git "a/XLM-RoBERTa.ipynb" "b/XLM-RoBERTa.ipynb"
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "executionInfo": {
+ "elapsed": 11742,
+ "status": "ok",
+ "timestamp": 1730569728288,
+ "user": {
+ "displayName": "Ismat Samadov",
+ "userId": "13714662825869203427"
+ },
+ "user_tz": -240
+ },
+ "id": "5v8KnAaD-z9t",
+ "outputId": "e58efe1c-a3de-4271-8e43-ecb8168a35de"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.44.2)\n",
+ "Collecting datasets\n",
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+ "Collecting seqeval\n",
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+ "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (0.24.7)\n",
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+ "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
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+ "Requirement already satisfied: fsspec<=2024.9.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from fsspec[http]<=2024.9.0,>=2023.1.0->datasets) (2024.6.1)\n",
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+ "Downloading datasets-3.1.0-py3-none-any.whl (480 kB)\n",
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+ "\u001b[?25hBuilding wheels for collected packages: seqeval\n",
+ " Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16161 sha256=0d4c765f01269491e0baddd39f053312bc7aa62fa5ead5b3e0dcb0bea5f36694\n",
+ " Stored in directory: /root/.cache/pip/wheels/1a/67/4a/ad4082dd7dfc30f2abfe4d80a2ed5926a506eb8a972b4767fa\n",
+ "Successfully built seqeval\n",
+ "Installing collected packages: xxhash, pyarrow, dill, multiprocess, seqeval, datasets\n",
+ " Attempting uninstall: pyarrow\n",
+ " Found existing installation: pyarrow 14.0.2\n",
+ " Uninstalling pyarrow-14.0.2:\n",
+ " Successfully uninstalled pyarrow-14.0.2\n",
+ "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+ "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 18.0.0 which is incompatible.\n",
+ "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 18.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
+ "\u001b[0mSuccessfully installed datasets-3.1.0 dill-0.3.8 multiprocess-0.70.16 pyarrow-18.0.0 seqeval-1.2.2 xxhash-3.5.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install transformers datasets seqeval huggingface_hub\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "amREIFSH-z7r"
+ },
+ "outputs": [],
+ "source": [
+ "# Standard library imports\n",
+ "import os # Provides functions for interacting with the operating system\n",
+ "import warnings # Used to handle or suppress warnings\n",
+ "import numpy as np # Essential for numerical operations and array manipulation\n",
+ "import torch # PyTorch library for tensor computations and model handling\n",
+ "import ast # Used for safe evaluation of strings to Python objects (e.g., parsing tokens)\n",
+ "\n",
+ "# Hugging Face and Transformers imports\n",
+ "from datasets import load_dataset # Loads datasets for model training and evaluation\n",
+ "from transformers import (\n",
+ " AutoTokenizer, # Initializes a tokenizer from a pre-trained model\n",
+ " DataCollatorForTokenClassification, # Handles padding and formatting of token classification data\n",
+ " TrainingArguments, # Defines training parameters like batch size and learning rate\n",
+ " Trainer, # High-level API for managing training and evaluation\n",
+ " AutoModelForTokenClassification, # Loads a pre-trained model for token classification tasks\n",
+ " get_linear_schedule_with_warmup, # Learning rate scheduler for gradual warm-up and linear decay\n",
+ " EarlyStoppingCallback # Callback to stop training if validation performance plateaus\n",
+ ")\n",
+ "\n",
+ "# Hugging Face Hub\n",
+ "from huggingface_hub import login # Allows logging in to Hugging Face Hub to upload models\n",
+ "\n",
+ "# seqeval metrics for NER evaluation\n",
+ "from seqeval.metrics import precision_score, recall_score, f1_score, classification_report\n",
+ "# Provides precision, recall, F1-score, and classification report for evaluating NER model performance\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "executionInfo": {
+ "elapsed": 6,
+ "status": "ok",
+ "timestamp": 1730569738838,
+ "user": {
+ "displayName": "Ismat Samadov",
+ "userId": "13714662825869203427"
+ },
+ "user_tz": -240
+ },
+ "id": "K7adlboI-z4p",
+ "outputId": "a4306ab9-b7d5-4108-e3f6-1edc4899694f"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n",
+ "Token is valid (permission: fineGrained).\n",
+ "Your token has been saved to /root/.cache/huggingface/token\n",
+ "Login successful\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Log in to Hugging Face Hub\n",
+ "login(token=\"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\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Qccgsjfs-zzA"
+ },
+ "outputs": [],
+ "source": [
+ "# Disable WandB (Weights & Biases) logging to avoid unwanted log outputs during training\n",
+ "os.environ[\"WANDB_DISABLED\"] = \"true\"\n",
+ "\n",
+ "# Suppress warning messages to keep output clean, especially during training and evaluation\n",
+ "warnings.filterwarnings(\"ignore\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 576,
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+ ]
+ },
+ "executionInfo": {
+ "elapsed": 23970,
+ "status": "ok",
+ "timestamp": 1730569762804,
+ "user": {
+ "displayName": "Ismat Samadov",
+ "userId": "13714662825869203427"
+ },
+ "user_tz": -240
+ },
+ "id": "fQ6ttUM8-zwM",
+ "outputId": "a7edc99a-0064-4378-ab18-c31f29c9b727"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "7de65e247932425882a27257bf77b913",
+ "version_major": 2,
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+ },
+ "text/plain": [
+ "README.md: 0%| | 0.00/2.87k [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
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+ },
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+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ "version_major": 2,
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+ },
+ "text/plain": [
+ "Generating train split: 0%| | 0/99545 [00:00, ? examples/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "DatasetDict({\n",
+ " train: Dataset({\n",
+ " features: ['index', 'tokens', 'ner_tags'],\n",
+ " num_rows: 99545\n",
+ " })\n",
+ "})\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "fba60dcd62c04b53acba3c371d926149",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Map: 0%| | 0/99545 [00:00, ? examples/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
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+ "Skipping malformed example: 03e9e957-da8f-45dc-84d0-e556bfd023b3 due to error: malformed node or string: None\n",
+ "Skipping malformed example: b7e12634-f7be-42cb-8e76-837af2f2d877 due to error: malformed node or string: None\n",
+ "Skipping malformed example: 0c77b0ac-b1cf-4730-ae3d-d7c59221f181 due to error: malformed node or string: None\n",
+ "Skipping malformed example: b4623202-dfcb-4fa8-9d28-5af818111de2 due to error: malformed node or string: None\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Load the Azerbaijani NER dataset from Hugging Face\n",
+ "dataset = load_dataset(\"LocalDoc/azerbaijani-ner-dataset\")\n",
+ "print(dataset) # Display dataset structure (e.g., train/validation splits)\n",
+ "\n",
+ "# Preprocessing function to format tokens and NER tags correctly\n",
+ "def preprocess_example(example):\n",
+ " try:\n",
+ " # Convert string of tokens to a list and parse NER tags to integers\n",
+ " example[\"tokens\"] = ast.literal_eval(example[\"tokens\"])\n",
+ " example[\"ner_tags\"] = list(map(int, ast.literal_eval(example[\"ner_tags\"])))\n",
+ " except (ValueError, SyntaxError) as e:\n",
+ " # Skip and log malformed examples, ensuring error resilience\n",
+ " print(f\"Skipping malformed example: {example['index']} due to error: {e}\")\n",
+ " example[\"tokens\"] = []\n",
+ " example[\"ner_tags\"] = []\n",
+ " return example\n",
+ "\n",
+ "# Apply preprocessing to each dataset entry, ensuring consistent formatting\n",
+ "dataset = dataset.map(preprocess_example)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
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+ "sentencepiece.bpe.model: 0%| | 0.00/5.07M [00:00, ?B/s]"
+ ]
+ },
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+ "output_type": "display_data"
+ },
+ {
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+ },
+ "text/plain": [
+ "tokenizer.json: 0%| | 0.00/9.10M [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
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+ "Map: 0%| | 0/99545 [00:00, ? examples/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Initialize the tokenizer for multilingual NER using XLM-RoBERTa\n",
+ "tokenizer = AutoTokenizer.from_pretrained(\"xlm-roberta-base\")\n",
+ "\n",
+ "# Function to tokenize input and align labels with tokenized words\n",
+ "def tokenize_and_align_labels(example):\n",
+ " # Tokenize the sentence while preserving word boundaries for correct NER tag alignment\n",
+ " tokenized_inputs = tokenizer(\n",
+ " example[\"tokens\"], # List of words (tokens) in the sentence\n",
+ " truncation=True, # Truncate sentences longer than max_length\n",
+ " is_split_into_words=True, # Specify that input is a list of words\n",
+ " padding=\"max_length\", # Pad to maximum sequence length\n",
+ " max_length=128, # Set the maximum sequence length to 128 tokens\n",
+ " )\n",
+ "\n",
+ " labels = [] # List to store aligned NER labels\n",
+ " word_ids = tokenized_inputs.word_ids() # Get word IDs for each token\n",
+ " previous_word_idx = None # Initialize previous word index for tracking\n",
+ "\n",
+ " # Loop through word indices to align NER tags with subword tokens\n",
+ " for word_idx in word_ids:\n",
+ " if word_idx is None:\n",
+ " labels.append(-100) # Set padding token labels to -100 (ignored in loss)\n",
+ " elif word_idx != previous_word_idx:\n",
+ " # Assign the label from example's NER tags if word index matches\n",
+ " labels.append(example[\"ner_tags\"][word_idx] if word_idx < len(example[\"ner_tags\"]) else -100)\n",
+ " else:\n",
+ " labels.append(-100) # Label subword tokens with -100 to avoid redundant labels\n",
+ " previous_word_idx = word_idx # Update previous word index\n",
+ "\n",
+ " tokenized_inputs[\"labels\"] = labels # Add labels to tokenized inputs\n",
+ " return tokenized_inputs\n",
+ "\n",
+ "# Apply tokenization and label alignment function to the dataset\n",
+ "tokenized_datasets = dataset.map(tokenize_and_align_labels, batched=False)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "executionInfo": {
+ "elapsed": 4,
+ "status": "ok",
+ "timestamp": 1730569818502,
+ "user": {
+ "displayName": "Ismat Samadov",
+ "userId": "13714662825869203427"
+ },
+ "user_tz": -240
+ },
+ "id": "DA7mW2it-zoo",
+ "outputId": "d4830123-d1c4-4056-8fcf-01d50ed580e9"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "DatasetDict({\n",
+ " train: Dataset({\n",
+ " features: ['index', 'tokens', 'ner_tags', 'input_ids', 'attention_mask', 'labels'],\n",
+ " num_rows: 89590\n",
+ " })\n",
+ " test: Dataset({\n",
+ " features: ['index', 'tokens', 'ner_tags', 'input_ids', 'attention_mask', 'labels'],\n",
+ " num_rows: 9955\n",
+ " })\n",
+ "})\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Create a 90-10 split of the dataset for training and validation\n",
+ "tokenized_datasets = tokenized_datasets[\"train\"].train_test_split(test_size=0.1)\n",
+ "print(tokenized_datasets) # Output structure of split datasets"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "-lVHfKEE-zmm"
+ },
+ "outputs": [],
+ "source": [
+ "# Define a list of entity labels for NER tagging with B- (beginning) and I- (inside) markers\n",
+ "label_list = [\n",
+ " \"O\", # Outside of a named entity\n",
+ " \"B-PERSON\", \"I-PERSON\", # Person name (e.g., \"John\" in \"John Doe\")\n",
+ " \"B-LOCATION\", \"I-LOCATION\", # Geographical location (e.g., \"Paris\")\n",
+ " \"B-ORGANISATION\", \"I-ORGANISATION\", # Organization name (e.g., \"UNICEF\")\n",
+ " \"B-DATE\", \"I-DATE\", # Date entity (e.g., \"2024-11-05\")\n",
+ " \"B-TIME\", \"I-TIME\", # Time (e.g., \"12:00 PM\")\n",
+ " \"B-MONEY\", \"I-MONEY\", # Monetary values (e.g., \"$20\")\n",
+ " \"B-PERCENTAGE\", \"I-PERCENTAGE\", # Percentage values (e.g., \"20%\")\n",
+ " \"B-FACILITY\", \"I-FACILITY\", # Physical facilities (e.g., \"Airport\")\n",
+ " \"B-PRODUCT\", \"I-PRODUCT\", # Product names (e.g., \"iPhone\")\n",
+ " \"B-EVENT\", \"I-EVENT\", # Named events (e.g., \"Olympics\")\n",
+ " \"B-ART\", \"I-ART\", # Works of art (e.g., \"Mona Lisa\")\n",
+ " \"B-LAW\", \"I-LAW\", # Laws and legal documents (e.g., \"Article 50\")\n",
+ " \"B-LANGUAGE\", \"I-LANGUAGE\", # Languages (e.g., \"Azerbaijani\")\n",
+ " \"B-GPE\", \"I-GPE\", # Geopolitical entities (e.g., \"Europe\")\n",
+ " \"B-NORP\", \"I-NORP\", # Nationalities, religious groups, political groups\n",
+ " \"B-ORDINAL\", \"I-ORDINAL\", # Ordinal indicators (e.g., \"first\", \"second\")\n",
+ " \"B-CARDINAL\", \"I-CARDINAL\", # Cardinal numbers (e.g., \"three\")\n",
+ " \"B-DISEASE\", \"I-DISEASE\", # Diseases (e.g., \"COVID-19\")\n",
+ " \"B-CONTACT\", \"I-CONTACT\", # Contact info (e.g., email or phone number)\n",
+ " \"B-ADAGE\", \"I-ADAGE\", # Common sayings or adages\n",
+ " \"B-QUANTITY\", \"I-QUANTITY\", # Quantities (e.g., \"5 km\")\n",
+ " \"B-MISCELLANEOUS\", \"I-MISCELLANEOUS\", # Miscellaneous entities not fitting other categories\n",
+ " \"B-POSITION\", \"I-POSITION\", # Job titles or positions (e.g., \"CEO\")\n",
+ " \"B-PROJECT\", \"I-PROJECT\" # Project names (e.g., \"Project Apollo\")\n",
+ "]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 105,
+ "referenced_widgets": [
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+ "userId": "13714662825869203427"
+ },
+ "user_tz": -240
+ },
+ "id": "jUfWCaen-zjr",
+ "outputId": "5c2a4470-3c9b-4f80-d841-24bff349c52f"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "61375655521643eba759627e20a995e2",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "model.safetensors: 0%| | 0.00/1.12G [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Some weights of XLMRobertaForTokenClassification were not initialized from the model checkpoint at xlm-roberta-base and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Initialize a data collator to handle padding and formatting for token classification\n",
+ "data_collator = DataCollatorForTokenClassification(tokenizer)\n",
+ "\n",
+ "# Load a pre-trained model for token classification, adapted for NER tasks\n",
+ "model = AutoModelForTokenClassification.from_pretrained(\n",
+ " \"xlm-roberta-large\", # Base model (multilingual XLM-RoBERTa) for NER\n",
+ " num_labels=len(label_list) # Set the number of output labels to match NER categories\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "9b7EajE_-zhS"
+ },
+ "outputs": [],
+ "source": [
+ "# Define a function to compute evaluation metrics for the model's predictions\n",
+ "def compute_metrics(p):\n",
+ " predictions, labels = p # Unpack predictions and true labels from the input\n",
+ "\n",
+ " # Convert logits to predicted label indices by taking the argmax along the last axis\n",
+ " predictions = np.argmax(predictions, axis=2)\n",
+ "\n",
+ " # Filter out special padding labels (-100) and convert indices to label names\n",
+ " true_labels = [[label_list[l] for l in label if l != -100] for label in labels]\n",
+ " true_predictions = [\n",
+ " [label_list[p] for (p, l) in zip(prediction, label) if l != -100]\n",
+ " for prediction, label in zip(predictions, labels)\n",
+ " ]\n",
+ "\n",
+ " # Print a detailed classification report for each label category\n",
+ " print(classification_report(true_labels, true_predictions))\n",
+ "\n",
+ " # Calculate and return key evaluation metrics\n",
+ " return {\n",
+ " # Precision measures the accuracy of predicted positive instances\n",
+ " # Important in NER to ensure entity predictions are correct and reduce false positives.\n",
+ " \"precision\": precision_score(true_labels, true_predictions),\n",
+ "\n",
+ " # Recall measures the model's ability to capture all relevant entities\n",
+ " # Essential in NER to ensure the model captures all entities, reducing false negatives.\n",
+ " \"recall\": recall_score(true_labels, true_predictions),\n",
+ "\n",
+ " # F1-score is the harmonic mean of precision and recall, balancing both metrics\n",
+ " # Useful in NER for providing an overall performance measure, especially when precision and recall are both important.\n",
+ " \"f1\": f1_score(true_labels, true_predictions),\n",
+ " }\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "PmJTMpp6-zew"
+ },
+ "outputs": [],
+ "source": [
+ "# Set up training arguments for model training, defining essential training configurations\n",
+ "training_args = TrainingArguments(\n",
+ " output_dir=\"./results\", # Directory to save model checkpoints and final outputs\n",
+ " evaluation_strategy=\"epoch\", # Evaluate model on the validation set at the end of each epoch\n",
+ " save_strategy=\"epoch\", # Save model checkpoints at the end of each epoch\n",
+ " learning_rate=2e-5, # Set a low learning rate to ensure stable training for fine-tuning\n",
+ " per_device_train_batch_size=128, # Number of examples per batch during training, balancing speed and memory\n",
+ " per_device_eval_batch_size=128, # Number of examples per batch during evaluation\n",
+ " num_train_epochs=12, # Number of full training passes over the dataset\n",
+ " weight_decay=0.005, # Regularization term to prevent overfitting by penalizing large weights\n",
+ " fp16=True, # Use 16-bit floating point for faster and memory-efficient training\n",
+ " logging_dir='./logs', # Directory to store training logs\n",
+ " save_total_limit=2, # Keep only the 2 latest model checkpoints to save storage space\n",
+ " load_best_model_at_end=True, # Load the best model based on metrics at the end of training\n",
+ " metric_for_best_model=\"f1\", # Use F1-score to determine the best model checkpoint\n",
+ " report_to=\"none\" # Disable reporting to external services (useful in local runs)\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "WqoF7QJy-zb2"
+ },
+ "outputs": [],
+ "source": [
+ "# Initialize the Trainer class to manage the training loop with all necessary components\n",
+ "trainer = Trainer(\n",
+ " model=model, # The pre-trained model to be fine-tuned\n",
+ " args=training_args, # Training configuration parameters defined in TrainingArguments\n",
+ " train_dataset=tokenized_datasets[\"train\"], # Tokenized training dataset\n",
+ " eval_dataset=tokenized_datasets[\"test\"], # Tokenized validation dataset\n",
+ " tokenizer=tokenizer, # Tokenizer used for processing input text\n",
+ " data_collator=data_collator, # Data collator for padding and batching during training\n",
+ " compute_metrics=compute_metrics, # Function to calculate evaluation metrics like precision, recall, F1\n",
+ " callbacks=[EarlyStoppingCallback(early_stopping_patience=5)] # Stop training early if validation metrics don't improve for 2 epochs\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "executionInfo": {
+ "elapsed": 1179788,
+ "status": "ok",
+ "timestamp": 1730571069005,
+ "user": {
+ "displayName": "Ismat Samadov",
+ "userId": "13714662825869203427"
+ },
+ "user_tz": -240
+ },
+ "id": "QveYYwvA-zUR",
+ "outputId": "a96f9658-96c1-479f-fa66-385d2e527103"
+ },
+ "outputs": [
+ {
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+ " precision recall f1-score support\n",
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+ " DATE 0.53 0.39 0.45 880\n",
+ " EVENT 1.00 0.10 0.19 96\n",
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+ " PRODUCT 0.82 0.84 0.83 2808\n",
+ " TIME 0.59 0.35 0.44 1569\n",
+ "\n",
+ " micro avg 0.78 0.69 0.73 30346\n",
+ " macro avg 0.71 0.54 0.58 30346\n",
+ "weighted avg 0.76 0.69 0.72 30346\n",
+ "\n",
+ " precision recall f1-score support\n",
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+ " ART 0.62 0.17 0.26 1857\n",
+ " DATE 0.46 0.47 0.46 880\n",
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+ " PRODUCT 0.82 0.85 0.83 2808\n",
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+ " macro avg 0.66 0.62 0.62 30346\n",
+ "weighted avg 0.74 0.74 0.73 30346\n",
+ "\n",
+ " precision recall f1-score support\n",
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+ " precision recall f1-score support\n",
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+ " macro avg 0.67 0.62 0.63 30346\n",
+ "weighted avg 0.75 0.74 0.74 30346\n",
+ "\n",
+ " precision recall f1-score support\n",
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+ "text": [
+ " precision recall f1-score support\n",
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+ "\n",
+ " micro avg 0.76 0.74 0.75 30346\n",
+ " macro avg 0.68 0.62 0.64 30346\n",
+ "weighted avg 0.75 0.74 0.74 30346\n",
+ "\n",
+ "{'eval_loss': 0.2521866261959076, 'eval_precision': 0.7643897128861069, 'eval_recall': 0.7404600276807487, 'eval_f1': 0.752234608817917, 'eval_runtime': 11.2348, 'eval_samples_per_second': 886.088, 'eval_steps_per_second': 13.885, 'epoch': 7.0}\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Begin the training process and capture the training metrics\n",
+ "training_metrics = trainer.train()\n",
+ "\n",
+ "# Evaluate the model on the validation set after training\n",
+ "eval_results = trainer.evaluate()\n",
+ "\n",
+ "# Print evaluation results, including precision, recall, and F1-score\n",
+ "print(eval_results)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "executionInfo": {
+ "elapsed": 2489,
+ "status": "ok",
+ "timestamp": 1730571071491,
+ "user": {
+ "displayName": "Ismat Samadov",
+ "userId": "13714662825869203427"
+ },
+ "user_tz": -240
+ },
+ "id": "7yEFe2_n-zPG",
+ "outputId": "622ba174-8cc7-4b8f-f5e5-a959a762360b"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "('./XLM-RoBERTa/tokenizer_config.json',\n",
+ " './XLM-RoBERTa/special_tokens_map.json',\n",
+ " './XLM-RoBERTa/sentencepiece.bpe.model',\n",
+ " './XLM-RoBERTa/added_tokens.json',\n",
+ " './XLM-RoBERTa/tokenizer.json')"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Define the directory where the trained model and tokenizer will be saved\n",
+ "save_directory = \"./XLM-RoBERTa\"\n",
+ "\n",
+ "# Save the trained model to the specified directory\n",
+ "model.save_pretrained(save_directory)\n",
+ "\n",
+ "# Save the tokenizer to the same directory for compatibility with the model\n",
+ "tokenizer.save_pretrained(save_directory)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "zkECg3v9-zNQ"
+ },
+ "outputs": [],
+ "source": [
+ "from transformers import pipeline\n",
+ "\n",
+ "# Load tokenizer and model\n",
+ "tokenizer = AutoTokenizer.from_pretrained(save_directory)\n",
+ "model = AutoModelForTokenClassification.from_pretrained(save_directory)\n",
+ "\n",
+ "# Initialize the NER pipeline\n",
+ "device = 0 if torch.cuda.is_available() else -1\n",
+ "nlp_ner = pipeline(\"ner\", model=model, tokenizer=tokenizer, aggregation_strategy=\"simple\", device=device)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "SOFqXU-M_bxO"
+ },
+ "outputs": [],
+ "source": [
+ "label_mapping = {f\"LABEL_{i}\": label for i, label in enumerate(label_list) if label != \"O\"}\n",
+ "\n",
+ "def evaluate_model(test_texts, true_labels):\n",
+ " predictions = []\n",
+ " for i, text in enumerate(test_texts):\n",
+ " pred_entities = nlp_ner(text)\n",
+ " pred_labels = [label_mapping.get(entity[\"entity_group\"], \"O\") for entity in pred_entities if entity[\"entity_group\"] in label_mapping]\n",
+ " if len(pred_labels) != len(true_labels[i]):\n",
+ " print(f\"Warning: Inconsistent number of entities in sample {i+1}. Adjusting predicted entities.\")\n",
+ " pred_labels = pred_labels[:len(true_labels[i])]\n",
+ " predictions.append(pred_labels)\n",
+ " if all(len(true) == len(pred) for true, pred in zip(true_labels, predictions)):\n",
+ " precision = precision_score(true_labels, predictions)\n",
+ " recall = recall_score(true_labels, predictions)\n",
+ " f1 = f1_score(true_labels, predictions)\n",
+ " print(\"Precision:\", precision)\n",
+ " print(\"Recall:\", recall)\n",
+ " print(\"F1-Score:\", f1)\n",
+ " print(classification_report(true_labels, predictions))\n",
+ " else:\n",
+ " print(\"Error: Could not align all samples correctly for evaluation.\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "executionInfo": {
+ "elapsed": 5,
+ "status": "ok",
+ "timestamp": 1730571073034,
+ "user": {
+ "displayName": "Ismat Samadov",
+ "userId": "13714662825869203427"
+ },
+ "user_tz": -240
+ },
+ "id": "WRCB-_66_buE",
+ "outputId": "c9a05d33-6fa4-4bae-93a0-04bf325683f8"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Precision: 0.5\n",
+ "Recall: 0.5\n",
+ "F1-Score: 0.5\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " LOCATION 0.00 0.00 0.00 0\n",
+ "ORGANISATION 0.00 0.00 0.00 1\n",
+ " PERSON 1.00 1.00 1.00 1\n",
+ "\n",
+ " micro avg 0.50 0.50 0.50 2\n",
+ " macro avg 0.33 0.33 0.33 2\n",
+ "weighted avg 0.50 0.50 0.50 2\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "test_texts = [\"Shahla Khuduyeva və Pasha Sığorta şirkəti haqqında məlumat.\"]\n",
+ "true_labels = [[\"B-PERSON\", \"B-ORGANISATION\"]]\n",
+ "evaluate_model(test_texts, true_labels)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "x53zS3Vv_brU"
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "5Uoebirj_boo"
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "RKounG2l_bl5"
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "authorship_tag": "ABX9TyNEIe3MLYmbuVlh01xxHiZi",
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+ "machine_shape": "hm",
+ "mount_file_id": "1EYYZa7dya2RjTZXHSJ4pzIOgzqR8lmSk",
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